From: AAAI Technical Report FS-98-01. Compilation copyright © 1998, AAAI (www.aaai.org). All rights reserved.
Towardsa Theory for a Sociable Software Architecture
Emmett Davis
(651) 699-4367
EmmettDa@aol.com
University of Saint Thomas
Graduate Programs in Software Mail # OSS301
2115 Summit Avenue
Saint Paul, MN55105
Bonnie Holte Bennett
(651) 962-5509
bhbennett@stthomas.edu
University of Saint Thomas
Graduate Programs in Software Mail # OSS301
2115 Summit Avenue
Saint Paul, MN55105
in discourse communities; and 3. meaning from its
intellectual dynamics over time. The representation of
these provides the basis for advanced, intelligent link
analysis.
The next section describes the technological and
commercial pressures on advanced information systems.
Section III describes the theoretical tenants underlying
our work. Section IV explores the benefits we see in
pursuing this approach. Section V describes an approach
to implementing this model. Section VI describes some
mechanismsin such an implementation.
Abstract
Thecomplexrelationship of networksof informationthat
exist on the weband in modem
computersystemsrequires
advancedlink-analysis capabilities in order to makesense
of the deluge of data. Applications are emergingwhich
use "intelligent agents"to gather data, however,they are
rarely morethan specially tuned search engineswith the
goal of gathering individual’s purchasing and chat
proclivities. Wepropose a link-analysis architecture
focusedon the individualwithattention on the individual’s
"discourse communities."Webelieve this focus, along
with the development and refmementof a discourse
communitythesaurus can uniquely begin to modelthe
social and cultural component’s
of a user’s profile. This
profile can help to beginto create moretruly intelligent
agentsperformingricher link analysis.
Motivation
This section describes a number of technological and
commercial factors which are putting extreme demands
on advancedinformation systems.
Business tempo is accelerating.
Exponential
information growth inundates us daily. Weexpect to
interact with smart systems that learn our preferences and
meet our specific needs. Wehope to "manage by wire"
where we communicate our intentions and our agents
"make it so." The "value chain" is identified as an
abbreviated business cycle in which highly customized
goodsarrive immediatelyas the consumerfeels the desire
for the product (with, ideally no time in the wholesaler’s
or the retailer’s hands, and thus, no inventory costs). As
organizations grow, they seek the optimal balance
between distributed operations and economiesof scale.
Agents abound on the web, but most seem to exist to
invade our privacy, not maintain is as all the predictions
of the electronic "Jeeves" have predicted. All of these
changes are dramatic and challenging for the information
systems they use, and they provide the motivation for
sociable software architecture.
Introduction
In order for advanced information systems to be truly
intelligent, they will need a social and cultural context.
Wewill need to understand and articulate the social and
cultural environmentsof each information system. In the
past information systems were isolated databases and the
extent of our architectural theory was normalization.
Then we were only plumbers linking pipes without regard
to content or on the opposite extreme workingin only one
domain.
Future systems will need to interact with a variety of
foreign systems. While object-oriented is a step forward
in dealing with these challenges, we need to be clear
about our philosophy relating to knowledgein a variety of
types of systems ranging from databases to artificial
intelligence and informationretrieval.
Beyondobject oriented programming, information and
knowledge require an explicit understanding of their
cultural and social environmentfrom which existing data
and information comes. We must articulate
how
information is used as knowledgeand howit is increased
or destroyed. For each information system we must
understand: 1. its scientific and academic discipline,
industry, or discourse community;2. the roles of actors
Exponential Information Growth Inundates Us.
Tractability, the combinatoric impact of large numbersof
interacting entities, increases the computationalresources
needed to complete a task. "The challenge posed to
businesses by the soon-to-be obscene amount of
information is staggering. Yet the promise is fabulous as
well, fueling hopes that knowledge management will
finally come of age." (Bovet and Sheffi 1998). While
Bovet and Sheffi are focusing on logistics,
other
examples, manyweb and agent related, easily come to
Copyright
©1998,American
Association
for ArtificialIntelligence
(x~av.aaai.org).
Allrightsreserved.
27
mind. Our desires to create space stations, anticipate the
weather, and predict geological cataclysms are instances
of our need to understand "large numbersof interacting
entities."
all levels of organizations, industry, and society as a
whole." (Gurbaxani and Whang1991). The effect is
beginning of managementby wire of complex industries
and value chains. Professionals will increasingly manage
by instructing systems of their intention, without giving
detailed orders.
Smart Systems Become an Exponential
Force
Multiplier. (Davis and Botkin 1994) describe smart
objects,"Products are smart because they filter and
interpret information to enable the use to act more
effectively .... Theyare interactive, they becomesmarter
the more you use them, and they can be customized."
(Davis and Botkin 1994). "The more you use knowledgebased offerings, the smarter the3, get." A knowledgebased system will learn more and put the newinformation
to productive use. The Ritz-Carlton hotel uses a customer
preference tracking system. All the hotels learn if a
customer expresses a preference for six hypoallergenic
pillows at one of the hotels. "The more you use
knowledge-based offerings, the smarter Fou get." An
example would be tutor systems that become group
enhanced decision support systems. "Knowledge-based
products and services adjust to changing circumstances."
Not only do smart products tell you something is wrong,
they also "tell you what to do." (Davis and Botkin 1994).
David and Botkin recognize that, "knowledge-based
businesses can customize their offerings." Smart systems
will allow large organizations
and alliances
of
organizations to ultra customize their services and
products. Smart systems act like compoundinterest, an
exponential force multiplier with growth.
Value Chain: an Abbreviated Business Cycle. The
value chain is a metaphor for all the processes and
information involved fi’om acquiring raw material,
creating products, and distributing them to customers."
Only interacting organizations, which deploy information
systems across supply chains, may survive. Increased
integration within the supply chain will "create better
information,
which in turn should support lower
inventories and improved financial performance. But the
evolution of IT and diminishingtransaction costs will also
lead to a fundamentalrestructuring of industry practices
for distributing and supporting products .... The biggest
impactof this restructuring will be on the intermediaries,
such as distributors and warehouses,that handle physical
goods and the information associated with them between
the producer and final consumer." (Lewis and
Talalayesky 1997).
Organizational
Behavior
is
Becoming
Interorganizational. "The contemporary competitive
environment requires interorganizational exchange and
coordination of logistics information to achieve common
goals .... The managerialand cultural aspects of logistics
are as critical as the impact of IT. While the emergence
of electronic data interchange (EDI) and electronic
markets would seem to permit an increase in the numbers
of suppliers firms can do business with, the opposite trend
seems more evident: firms are developing close
relationships with a small numberof major suppliers. For
example, Wal-mart’s in-store inventories of items such as
Tide and Crest are managed by Procter & Gamble, who
have direct access to point-of-sale data." (Lewis and
Talalayesky 1997)
Suppliers manage inventories
sometimeswithout any intervention by the retailer. In the
retail grocery industry, the movementtoward "efficient
consumer response" seeks to more precisely match
inventory levels at the manufacturer, distributor and
retailer to actual demand."Risks (such as transaction risks
and agency costs) remain a real issue: how much
information should be shared with suppliers? Should
suppliers provide their customers with details of their
manufacturing processes? What will happen if suppliers
are allowed to see information on their competitors? A
final issue is the developmentof performance indicators
and reward mechanisms for managing partnerships
between producers, distributors,
retailers
and
transportation firms. For example, buyers and suppliers
may need to decide how they will share reductions in
Managementby Wire: Carry Out my Intentions. In
aviation "flying by wire" is a term that indicates that
computersystems are used to augmenta pilot’s ability to
assimilate and react to rapidly changing environmental
information.
"By-wire" systems present selected
abstractions of a few crucial environmental factors.
Instrumentation and communicationtechnologies aid in
evaluating alternative responses. Computer system
intercepts the pilot’s commandand translates it into
thousandsof detailed orders that orchestrate the systemin
real time. Whenpilot’s fly by wire, they’re flying
information representations of airplanes. The key is that
the instructions are intentions, not orders. In a similar
way"managingby wire" is the capacity to run a business
by managing its informational
representation.
"Information systems have reduced decision information
costs by allowing decision makerscost-effective access to
information and powerful tools (e.g., simulation and
econometric modeling) for analyzing the retrieved
information. The improvementin decision quality in turn
increases operational efficiency. For example, accurate
forecasting of future demands, coupled with efficient
handling of material flows and production scheduling, can
achieve a significant reduction of inventory costs. Indeed
the impact of this information revolution has been felt at
28
transportation and inventory costs. "
Talalayesky 1997).
(Lewis and
Representation: Internal and External Internalize the Social, Externalize the Web
Agency Theory. The reshaping of players in markets,
including the demise of somefirms, means that people’s
behavior will be changed. Information systems are
crucial to this process. The alliances, which arise from
value or supply chain management,must be supported by
interorganizational information linkages. Thesealliances,
as had electronic data interchange linkages, represent a
relationship commitmentthat moves transactions away
from the open market. These transactions require that
organizations decide to implement interorganizational
information technology. One example of the impact of
information technology is in the size of organizations.
According to Gurbaxani and Whang"firm size and the
allocation of decision-making authority among the
various actors in a firm are, to a considerable degree,
determined by the costs associated with acquiring,
storing, processing and disseminating information."
(Gurbaxani and Whang 1991). Gurbaxani and Whang
"argue that as decision-making rights are pushed
downwardin the organizational pyramid, the costs of
communicating information upward decrease while
agency costs resulting from goal divergence increase.
Therefore, decision rights in an organizational hierarchy
should be located where the sum of these costs is
minimized. Modem IT can reduce the costs of
communicatinginformation by improving the quality and
speed of information processing and management’s
decision making, leading to more centralized
management. At the same time, IT can also provide
managementwith the ability to reduce agency costs
through improved monitoring capabilities
and
performance
evaluation
schemes,
inducing
decentralization of decision making." (Gurbaxani and
Whang1991).
A rich debate about "representation" has swirled for
decades. In the most recent chapter, RodneyBrooks has
asserted that, "Representation is the wrong unit of
abstraction in building the bulkiest parts of intelligent
systems." (Brooks 1991). In another work, he states that
in his robot creatures, "There are representations, or
accumulationsof state, but these only refer to the internal
workings of the system; they are meaningless without
interaction with the outside world." (Brooks 1998). The
only external representation or models of the world are
those determined to be useful. Andthese representations
are stored in the world, not in the agent. For sociable
software architectures, accumulations of compresseddata
and evaluations on its conclusions are internal
representations of the system’s history. Meaningabout
the "real" worldis stored in the world, in the history and
projected interactions with humans. Meaningis derived
only from interactions with real world. A side effect is
that we are well prepared to support knowledgesystems.
Knowledgeis potential action based on information, data
with meaning.
Aninsight we wish to contribute is that words (strings
of characters and their vocal counterparts) are of the real
world. Wetreat words as real objects, but not just as
external representations of meaning. Wordsare data and
not information.
We must build meaning, create
information, from local interactive processes with this
data. While Brooks appropriately rejects internal
representations (which would seem to include words) for
robots, we treat them as sensor data, and the social
interactions they indicate as the "real world". Is our
culture and society any less real than gravity and physical
objects? Artificial intelligence can "movearound" our
culture as easily as it does whenBrooks’ creatures move
aroundthe halls of his labs. For both of us actions are the
crucible. Only words are part of the "outside world" just
as sonar readings.
Theoretoical
Tennants for Sociable Software
Architecture
This section describes the theoretical tenants motivating
our work.
The issues discussed in the previous section indicate an
important change in demands on technology.
As
information grows exponentially, as expectations grow to
manage by wire, as the value chain tightens,
as
organizational
behavior and information sharing
increases, and as agency theory exerts its pressure in
humanas well as algorithmic manifestations, there will be
a need for software with an ability to interact in a richer
social context. As we explore "sociable" software
architecture our core concern is our concept and treatment
of representation. What do the symbols we manipulate
represent?
How does one determine meaning? Is
meaningstable over time?
Discourse Communities Model the Social
Realities of Interactions
This observation has interesting implications when
viewedin light of Hjorland’s observations in information
science. Specifically, that "discourse communities"
provide a tool for analysis,
comprehension, and
processingof social interactions.
Following pragmatic objectivity, we hold that there is
no intrinsic meaning for knowledge or information in
isolation from action. Information’s meaning is neither
subjective nor relative. Pragmatic objectivity is based on
action theory/utility and a collective methodology.
29
Objective idealism will search subject matter in
permanent,inherent characteristics of knowledge,or
in permanent,inherent semantic relationships and try
to establish standardized, permanent, fixed ways of
analyzing relationships (disregarding their potential
use). Subjective idealism, on the other hand, will
search for the subject matter of a documentin either
the author’s or the users’ subjective perception of the
document and try to develop a theory of subject
analysis based on either the author’s psychological
world (as in classical hermeneutics) or the users’
psychological world (as is done in parts of modern
cognitive viewpoint).
on perceptions of these objects can learn to create useful
behavior. "The key idea ... is to be using the world as its
own model and continuously match the preconditions of
each goal against the real world." (Brooks 1991).
Semantics arise from the success or failure of actions
based on outcomes of processes performed on the raw
data. There is no intrinsic meaningor semantic content
before this. There is no external representation except for
"maps"and "behavior" in sociable software architecture.
Social Interaction:
Fromthe position of activity theory, we do not look
on subject as either inherent characteristics or as
something subjective
in an individual
way.
Individual subjects’ behaviorin relation to use and to
representation of information should be interpreted
in the light of a (sub)disciplinary context. (Hjorland
1997).
Meaningis social and collective, that is, weuse a
methodological collectivism methodology. Weneed
... to see knowledge as a developed historicalcultural-social product. The alternative to studying
individual subjects and individual informationseeking behavior is to study knowledge domains.
That is to study their informational structures, their
terminology,
knowledge
representation,
communicationpatterns, all in connection with their
theories of knowledge and theories of science.
(Hjorland 1997)
The discourse communityfor Hjorland’s information
systems is an academic discipline. This generalizes to
business and culture for systems that are more than
information retrieval systems. The discourse community
becomescentral. It is the sole source of meaning.Based
on observing a discourse community’saction over time
and by interacting with it, sociable software architectures
can use and create information. Given a discourse
community as our unit of work, and not just the
individual, we becomeaware of the roles individuals have
within the discourse community. Our approach must
therefore be collectivist. The challenge is to implementa
recording of cultural and social environment and
behavior. If we can do this, we have a basis for
intelligence. Again the key to intelligent meaning is
action, not representation of a permanentworld.
For Brooks’ creatures, there is intelligence without
external representation. They operate in a real, sensory
world with humans. Weassert that the Internet and its
databases are also a real, sensory world with humans.It
is a world filled with wordsand figures. Agentsoperating
3O
Mentorship for Learning
Because we need meaning for information and knowledge
and meaning comes from interaction with humans, social
interaction is vital to sociable software architecture.
Social interaction can be structured in a variety of ways,
but the most basic is that of mentorship. For elemental
interactions with one human,systems should allow for a
mentor. Sociable software architecture will need a
mentor the way Star Trek’s Data needed Dr. Su. The
mentor provides artificially
structured or meaningful
interactions. Brooks recognized this need when he used
scaffolding and feedback for his creatures. Scaffolding
and feedback allow humansand other creatures to learn
from humans. The learning process allows us to share
attention with the care giver. The care giver in return
narrows our focus to what is relevant and most helpful in
learning about the matter at hand.
While sociable software architecture’ architecture will
change its clustering or conclusions depending on human
evaluation of the proposals, sociable software architecture
has two other methodologies for mentors to use. Since
sociable software architecture consists only of rules and
data, a mentorcould directly intervene in one or the other.
The use of Lenat’s Cyc, for example, is an addition that
would improve sociable software architecture’
discernment of words. (Lenat and Guha1990).
For sociable software architecture, there are many
individuals in manydiscourse communities and there are
roles for these individuals in their discourse communities.
In either case, learning from humansis basic.
Roles in the Discourse Community:Innovators, Trend
Setters, Maintainers. Because meaning is not just
psychological, but is also sociological, we need a social
unit of meaning of work. The discourse community is
that unit of meaning. While there are agents for
individuals in sociable software architecture and the
individual
contributes
to meaning (especially
trendsetters), the individual is not the determiner of
meaning. The meaning we seek to understand is not
subjective at either the author’s or the consumer’send.
Discourse communities though are composed of many
individuals. Each individual mayhave one or more roles
and these will change over time. The challenge will be to
first place an individual in a discourse communityand
then determine what roles they play. Someindividuals
are innovators and early change agents. Other individuals
are successful trend setters and while not the first to
innovate, when they do so, their new direction will
usually be successful. Other individuals are maintainers
of the discourse community. These individuals provide
the instruction needed by new membersto adapt to the
existing discourse community. That an individual
operates in several domainsor discourse communitiesis a
source of richness, appropriately gray, however,
knowledgeof an individual’s membershipin a discourse
community and of their role in it is useful to the
individual’s agent in predicting interest in newsites, for
example, if I am a trendsetter in a Star Trek community
and an increasing numberof innovators have been visiting
and re~rning to "www.foo.com"
then myagent is safe in
directing meto it.
Improving
Problem Solving
Capacity
This section explores the benefits we see in pursuing this
technical approach.
An analysis of the potential improvementsto problem
solving, consider the problems trying to f’md something
on the web. Current web searches return thousands of
hits to a query. Even meta searches are inadequate since
the best they can do is showthe intersection of individual
searches. Mysearch engine needs to know about me,
what I do, what I’m interested in, and I’d like it to work
overtime finding interesting sites, calling myattention to
them, and not just selling menew things. I’d like it to
learn about me, but to keep that information private, and
not share it with vendors so they can bombard me with
email, and slot me into the appropriate banner add
categories. I’d like myweb search agent to be proactive,
have good recall and f’md related work, and to guard my
privacy. If it can learn by sharing information with other
agents, fine, I’d like it to learn to become more
customized, but all the while, it must safeguard my
privacy.
Changes Over Time are Important. Current web agents
are either one time use, or require registration and its
subsequent loss of privacy. Ideally, users would like
persistent web agents under their control. Time is
significant for discourse communities.
There are several forms of time: online and offline;
evolution of individual, community, individual in
community; and world of words and of sensory data of
the world of words. Two forms of time that are
particularly important are the evolution of discourse
community’s meaning and the interaction time with
membersof a discourse community.
The evolution of the discourse community’s meaning
must be tracked. Paradigm shifts can sometimesrapidly
occur. Changesin legislation or regulation, for example,
can shift the meaning of what had been a stable
representation of the world. Becausea sociable software
architecture stores its representation of the world’s model
in the world, sociable software architecture is both
vulnerable to shifts and should be accurate in tracking
them.
The interaction time of the individual is entirely the
business of the individual’s agent and is entirely personal.
Understanding this sense of time will allow sociable
software architectures to respond to queries in a "getback-to-you" modewhen queries require a bit of time.
Also, sociable software architectures will need to
understand whento present innovations or changes, that
is, whento anticipate a query.
Addedto all these processing times, we still have the
time frames inherent in the raw source data. Therefore
there is a need for data fusion and knowledge
representation structures such as blackboards. While not
significant
in all situations,
sociable software
architectures will need to understand whether a new
datumis a confirmation of a knownfact or a newfact.
Anticipating Needs with Proactive Models. Many
current information-seeking systems rely on a query
before processing and returning solutions. Wepropose
proactive solutions either triggered by a specific query or
more often searching based on both past queries, probable
queries, and queries of similar users. If the system
designer considers the solution space as a social epistemic
space, this becomes feasible. Information retrieval
system, rather than searching for a subject string indexed
to a solution, should consider Hjorland’s statement that:
"The subject of a document is the epistemological
potential of that document."(Hjorland 1997).
Improving Recall and Relevance. Doing so will
improveboth the recall and relevance and the timeliness
of the solutions offered by systems. If we evaluate
solutions using ecological behavior, we have a more
robust evaluation system. "The understanding of an
individual’s optimal choice in information seeking is
based on some conditions about the ecosystem in which
that individual forms a part." (Hjorland 1997).. If, for
example, we evaluate the content of a representation on
the basis of whetherit can contribute to the solution of a
concrete problem or satisfy a need (such as one or more
of Maslow’s hierarchy of needs), then we can search
larger space continually, even before a specific query is
asked.
This would improve retrieval time of very large and
distributed
stores and processes. This allows for
spontaneous pre-processing
and continuous agent
clustering of potential solution spaces. It permits use of
local syntax and representation of potential solution
31
spaces. A sociable architecture also provides the
robustness to handle diverse time frames and sources
through data fusion.
In order to handle the complexity and ultra-customization
demanded by the people providing our motivation, we
needed an implementation that was intrinsically
distributed.
Within this n-tiered system, universal structures and
operations occur.
Data compression, distributed
processing, background processing, and data stores are
resulting benefits.
Maintaining Privacy. Unlike current systems, it is
both possible and advantageous to implement privacy of
discourse communityindividual members.Solution paths
are through clusters’ of members’behavior separate from
their identity. Perhaps if the current systems had been
originally built by consumersinstead of vendors, there
would be fewer added process and data store baggage of
collecting individual identities in order to actively
approach individuals to buy more services and products.
Source Agents
Source agents work with the raw source data whatever it
maybe. In interactive systems this includes tracking the
bulk data about use of source data by users. These source
agents make the first approximation of sorting with
demographics and other structural clues the source and
use data into discourse communities.
The source agents have discourse communitymaps and
trees of their characteristics and overlap of these clusters.
The source agents use a variety of tools including
statistical,
and node and link analysis. This analysis
includes citation analysis whereappropriate against a tree
of citations. "Citations" is used here in a very general
sense. In addition to the usual bibliographic citations, we
include hot links in html sources and even borrowed
vocabulary or techniques, if they and their source can be
identified.
This identification and clustering essentially becomes
input to one or more discourse community agents via
blackboardsand other structures.
Improving Efficiency with Distributed
Processing. Amongthe pre-conditions of such social
cognitive systems are a memory
of similar others’ use and
the ability to identify similar others. Advantageous
in
today’s networkedsystems wouldbe distributed stores,
pre-processing and processing behaviors. Agents need to
be able to evaluate user, vendor, and system data and
behavior. Agents need to be able to seek information
with or without a query.
Customized to Individual Users’ Needs. Two postconditions are the capability to contact an individual in an
appropriate style as chosenby the individual and to accept
evaluation of the by the individual, both directly and by
inference. Appropriate access includes language prerequisites and formats.
Because of the need for sociable systems, manytypes
of information systems must link with each other:
databases and data warehousing systems; information
retrieval applications; and artificial intelligence and
decision support systems. Link analysis agents with
intent controls own execution, and object oriented
programming systems especially with polymorphism at
trans-componentlevel are indications of this need. Also
importantwill be the treatment of time.
Implementation
Discourse
Community Agents
Throughblackboards and other structures the agents use
for a discourse communityare available to source agents.
Both sets of agents share manyof the same resources,
which are distributed so that discourse communityagents
use the portions of the resources usually needed by them
without drawing downthe whole resource pool.
An example of this is the thesaurus. Certainly tools
such as Lenat’s CYCwill provide a foundation, but the
thesauri will grow through interaction betweensource and
usage data. A more permanent thesaurus, or at least one
with a longer historical memory, will be the metathesaurus where higher level patterns are stored. The
thesauri will be trees within trees, a terminology semihypercube. The discourse communityagent thesauri are
the keys to the compresseddata files created by source
and other agents.
The discourse communityagents and their data stores
and thesauri are distributed
by community. Some
communities are themselves distributed,
others are
centralized.
Model
This section describes an approach to implementing a
sociable software architecture.
Sociable systems must interact with the primary users
they serve. Often the primary users are humans. Humans
learn and change the rules with which systems must stay
abreast. Humansalso define the meaningof the reality or
world sensible to systems. Often this sensible reality is
data. The systems then have processes to use on the data,
often transforming data into information and at times
knowledge.
Weuse an n-tier agent model, consisting of at least
source, discourse community,and individual user agents.
32
Individual’s
Agents
Each individual served has an agent that understands that
the individual is a member of several discourse
communities and has different roles in each community.
It is the partnership of the discourse community
agent and
the individual’s that generates the anticipatory responses
from the individual’s agent.
It is the individual’s agent that receives the queries and
the evaluations to the responses to queries. The
individual’s agent maintains the data identifiable to the
individual. This agent is the one that creates and protects
the individual’s privacy.
This agent is also the
maintainer of the individual’s personal thesaurus as well
as the sense of appropriate time and formats for
interacting with the individual. In an extreme example,
this agent maycommunicatewith the individual solely
through another person. Instances might include the
parents of infants.
Models of the Discourse Communities
This section describes some of the mechanismsused to
implement a sociable software architecture. Wedepend
on similarity measuresor classification using vectors in
an n-dimensional space. As source agents conjure a new
discourse community or discourse community agents
decide that a discourse community is splitting into
separate communities, we create new agents for the new
discourse community.
The models of the discourse are multi-dimensional
arrays. Vector analysis of the array dimensions provides
us with a discourse community’scentroid or fingerprint
characteristics. This includes the features of clusters of
individuals performing a role within the discourse
community.
Lately we have been experimenting with compressing
the data store by transformingsimilar data into layers of a
tree. Thesewill be shared across agents as a source of the
"meta knowledge"about the individual’s membershipin
and roles related to discourse communities. Three
benefits can be derived from this: data compression,
facilitation of distributed processing, and background
processing.
The compression highlights similarities as well as
outlier patterns. Wethen look for the rare patterns that
over time becomemainstream. The individuals attached
to the once rare patterns are the trendsetters. If their role
persists, then we can view probable developmentsfor the
community.
Data Compression Derives Naturally from the
Use of Discourse Community Thesauri
All the agents regardless of level, create and use
compressed data. Wecan compress data in such a way
that it does not need to be uncompressedfor operations to
occur. Inspired by wavelets, we maybe able to compress
data so that the differences are highlighted, rather than
obscured.
A classic discourse community example is the
collectors of Eighteenth Century Hong Kong stamps.
Whena discourse communityis well defined, then the
compression will be tighter. Essentially we create an
array string of what is most similar. Wethen remove
these dimensions from the arrays of all members and
create a pointer to what is similar, or note differences with
the similarity standard. The identifier array for Hong
Kongstamps will overlap with those of stamp collectors
and those interested in the history of HongKong.
Thesauri terms and links most attached to the
fingerprint array becomesthe key to the compression of
source data. Weuse the fingerprint thesauri to store the
similarities. This allows us to also restore the raw data to
a specified level. This creating, dissembling, and reassembling initially will be common
as the agents create
hypotheses that they later abandon.
Because the thesauri and the compressed data are
actually the internal representation of the world, other
agents and entities may use them as sociable software
architecture evolves.
Compartmentalization of Thesauri Facilitates
Distributed and Background Processing
The distributed architecture of sociable software
architecture meansthat we must use techniques such as
information fusion. Rawdata will arrive from a variety of
sources in a variety of modes. Blackboardsand other
such mechanismswill be used by sociable software
architecture agents to communicate
clearly on the details
of the rawdata.
This will be important for all the agents at every level,
but especially true for the source agents whoare mining
data streams.
Conclusions
The complexrelationship of networks of information that
exist on the web and in moderncomputer systems
requires advancedlink-analysis capabilities in order to
makesense of the deluge of data. A numberof factors
contribute to this including exponential information
growth demandfor smart systems, desire to "manageby
wire," the abbreviated value, and dynamicbusiness
organizations. The agents currently available on the web
33
seem to have no memoryand exist only to exploit
privacy. Wepropose a link-analysis architecture focused
on the individual with attention on the individual’s
"discourse communities."Webelieve this focus, along
with the developmentand ref’mementof a discourse
communitythesaurus can uniquely begin to modelthe
social and cultural component’sof a user’s profile. This
profile can help to begin to create moretruly intelligent
agents.
The goals of this paper have been to begin to delineate
the pressures towards more advanced intelligent agents
which will provide a richer frameworkfor link analysis,
to survey somerelated work from information theory, and
to suggest an architecture which might be useful. We
suspect that we’ve opened more questions than we’ve
answered, and (an admirable thing to do in some
respects), and we hope that our further research will
continueto address these pertinent issues.
References
Bovet, D. and Sheffi, Y. "The Brave NewWorld of
Supply Chain Management." Supply Chain Management
Review. Vol 2. No. 1 Spring 1998. 14-22.
Brooks, A. Rodney,et al.. "Alternative Essences of
Intelligence" in Proceedings:Fifteenth National
Conferenceon Artificial Intelligence (AAA1-98)and Tenth
Conferenceon Innovative Applications of Artificial
Intelligence (IAAI-98) 961-968. MenloPark, CA: AAAI
Press/MITPress, 1998.
Brooks, R., "’Intelligence without representation,"
Artificiallntelligence, vol. 47, 139--160,Jan. 1991.
Byrne, S. M. and Javad, S. "Integrated Logistics
Information Systems (ILIS): Competitive Advantage
Increased Cost?" Annual Conference Proceedings (Oak
Brook, IL: Council of Logistics Management,1992). 5573.
Davis, S. and Botkin, J. "The Comingof KnowledgeBased Business." Harvard Business Review. Sept-Oct
1994. 165-170.
Gurbaxani, V. and Whang,S. "The Impact of
Information Systems on Organizations and Markets."
Communications oftheACM. Vol. 34. No. 1. January
1991. 59-73.
Hjorland, B. Information Seeking and Subject
Representation: An ActiviO,-Theoretical Approachto
Information Science. Westport, CT: GreenwoodPress,
1997. ISBN: 0-313-29893-9
34
Lenat, D.B. and Guha, R.V. "Building Large
Knowledge-BasedSystems", Addison-Wesley, Reading,
MA,1990. ISBN0-201-51752-3.
Lewis, I. and Talayevsky, A.. "Logistics and Information
Technology: A Coordination Perspective." Journal of
Business Logistics. Vol. 18. No. 1. 1997. 141-157.